Generally speaking, processes used in power plants, industrial manufacturing plants, material processing plants and other types of plants include one or more controllers communicatively coupled to a plurality of field devices via analog, digital, combined analog/digital, or wireless communication channels or lines. The field devices, which may be, for example, valves, valve positioners, switches, transmitters (e.g., temperature, pressure, level and flow rate sensors), burners, heat exchangers, furnaces, boilers, turbines, etc., are located within the plant environment and perform process functions such as opening or closing valves, measuring process parameters, generating electricity, burning fuel, heating water, etc., in response to control signals developed and sent by the controllers. Smart field devices, such as the field devices conforming to any of the well-known Fieldbus protocols may also perform control calculations, alarming functions, and other functions commonly implemented within or by a plant controller. The plant controllers, which are also typically located within the plant environment, receive signals indicative of process measurements made by the field devices and/or other information pertaining to the field devices and execute a control application that runs, for example, different control modules which make process control decisions, generate process control signals based on the received information and coordinate with the control modules or blocks being performed in the field devices, such as HART® and FOUNDATION® Fieldbus field devices. The control modules within the controller send the process control signals over the communication lines or networks to the field devices to thereby control the operation of the process.
Information from the field devices and the controller is usually made available over a data highway to one or more other computer devices, such as operator workstations, personal computers, data historians, report generators, centralized databases, etc., typically placed in control rooms or at other locations away from the harsher plant environment. These computer devices may also run applications that may, for example, enable an operator to perform functions with respect to the process, such as changing settings of the process control routine, modifying the operation of the control modules within the controllers or the field devices, viewing the current state of the process, viewing alarms generated by field devices and controllers, implementing auxiliary processes, such as soot-blowing processes or other maintenance processes, keeping and updating a configuration database, etc.
As an example, the Ovation® control system, sold by Emerson Process Management, includes multiple applications stored within and executed by different devices located at diverse places within a process plant. A configuration application, which resides in one or more engineer/operator workstations, enables users to create or change process control modules and to download these process control modules via a data highway to dedicated distributed controllers. Typically, these control modules are made up of communicatively interconnected function blocks, which are objects in an object oriented programming protocol, which perform functions within the control scheme based on inputs thereto and provide outputs to other function blocks within the control scheme. The configuration application may also allow a designer to create or change operator interfaces which are used by a viewing application to display data to an operator and to enable the operator to change settings, such as set points, within the process control routine. Each of the dedicated controllers and, in some cases, field devices, stores and executes a controller application that runs the control modules assigned and downloaded thereto to implement actual process control functionality. The viewing applications, which may be run on one or more operator workstations, receive data from the controller application via the data highway and display this data to process control system designers, operators, or users using the user interfaces. A data historian application is typically stored in and executed by a data historian device that collects and stores some or all of the data provided across the data highway while a configuration database application may execute in a still further computer attached to the data highway to store the current process control routine configuration and data associated therewith.
In many industries however, it is desirable or necessary to implement a simulation system for simulating the operation of a plant (including the various plant devices and the control network as connected within the plant) in order to perform better control of the plant, to understand how proposed control or maintenance actions would actually affect the plant, etc. Such a simulation system may be used to test the operation of the plant in response to new or different control variables, such as set-points, to test new control routines, to perform optimization, to perform training activities, etc. As a result, many different types of plant simulation systems have been proposed and used in process plants.
In the field of process plant simulation, process simulator design is typically based on either a first principle-based model or an empirical data-based model. A first principle-based model, also called a high-fidelity model, models equipment and processes based on first principle physical laws, such as well known mass, energy, and momentum conservation laws. First principle-based models describing a physical process are often complex and may be expressed using partial differential equations and/or differential algebraic equations. These equations may describe process or equipment properties and/or changes in those properties. In many first principle-based models, equations are modular which enables these equations to model specific pieces of equipment and/or processes in a multi-equipment or multi-process system. Thus, equipment and/or processes can be easily changed and/or updated in the model by replacing equations in the model with equations corresponding to the changed and/or updated equipment and/or processes. However, first principle-based models are subject to modeling errors due to the inability of first principle-based models to account for uncertainty surrounding the actual characteristics or properties of process equipment or of the process environment at any particular time. These process characteristics are, in many cases, simply estimated by a plant operator or are estimated using some other manual or off-line estimation technique.
On the other hand, empirical data-based models, also commonly called black-box models, generate modeling formulas or equations by applying test inputs to an actual process system in accordance with a designed experiment and then measuring test outputs corresponding to the test inputs. Based on the inputs and outputs, equations or other models that define a relationship between the inputs and outputs are generated to thereby create a model of the process or equipment. In this approach, the empirical equations may be easier to obtain than first principle-based equations, and dynamic transient phenomena may be better captured and represented in the empirical equations than in first principle-based equations. However, special experiments must be designed, implemented, and executed to acquire the accurate and diverse data sufficient to generate the empirical data used to develop the model. Moreover, the plant must typically be operated over some period of time in order to develop the model, which can be expensive and time consuming. Further, when equipment is changed or replaced, new empirical models must be developed, which can also be time consuming and costly. Still further, empirical models are unable to account for changes in the plant environment or for slow or gradual changes in the process plant equipment that result from aging or use of the plant equipment. In other words, while empirical based models are able to account for inherent process characteristics at the time of generation, these models are unable to be altered easily to account for changes in the process characteristics over time.
Thus, regardless of the type of process modeling approach used, a process simulation model often needs tuning and/or adjustment to be accurate enough for the purposes of the simulation in which the model is used. For example, in many cases, the created simulation models include factors related to unmeasurable process variables or characteristics (e.g., inherent properties of the process or the process equipment), referred to herein as process characteristic parameters, that change over time due to, for example, wearing of equipment, changes in the plant environment, etc. An example of one such process characteristic parameter is a heat transfer coefficient used to model heat exchangers within a plant, although there are many other such process characteristic parameters. In many cases, while these process characteristic parameters are unmeasurable as such in the plant, it is important to be able to determine these process characteristic parameters accurately, as the values of these process characteristic parameters not only affect the accuracy of the simulation, but may also be used to make decisions to perform other actions within the plant, such as control actions and maintenance actions.
As an example, many power plant processes (as well as other types of process applications) utilize heat exchangers that operate to transfer thermal energy from one fluid medium to another fluid medium as part of the power generation process. It is important for simulation and control purposes to determine how much energy is being transferred between the fluids at any particular time so that the equipment efficiency and the resulting temperature change can be accurately simulated, evaluated or understood, to thereby be able to determine appropriate control and/or maintenance actions. It is known that the heat transfer efficiency of heat exchangers and the resulting medium temperature changes within a heat exchanger are largely affected by the material properties of the heat exchanger (such as heat conductivity and heat capacitance of the heat exchanger), the heat exchanger surface area, the thickness of the heat exchanger tubes, the heat exchanger geometry, and various run-time conditions. Among these factors, material property, surface area, tube thickness, and configuration geometry can be considered to be design data from which a “design” heat transfer coefficient for a heat exchanger can be determined based on known mathematical principles. However, design information usually provides only a coarse approximation to the actual heat transfer coefficient of a particular heat exchanger being used in a plant at any particular time. The reason that the design heat transfer coefficient and the actual heat transfer coefficient of a heat exchanger as used in a plant differ is that the design data does not account for other, typically changing, factors present within a process plant that alter or affect the heat transfer coefficient, and thus affect or alter the heat exchanger efficiency during operation of the plant. In fact, the actual run-time environment typically includes many different factors that directly impact the “actual” heat transfer coefficient, which in turn affects the “actual” heat transfer efficiency and the “actual” final temperature of the fluid exiting the heat exchanger. For example, as the result of harsh coal combustion and fly-ash within a flue gas entering into a heat exchanger, soot builds up on or deposits on the surfaces of heat exchanger, and the heat transfer characteristics of this soot greatly affects the efficiency of and temperature changes produced within the heat exchanger. In addition, soot build-up and soot-blowing operations (that are implemented from time to time to remove the soot build-up within a heat exchanger) change the thickness of the tubes over time, which also affects the efficiency and temperature profile of the heat exchanger. Thus, the “actual” heat transfer coefficient for a heat exchanger used in process simulation and control needs to be adjusted or tuned to be different than the design heat transfer coefficient value for that heat exchanger to account for unmeasurable factors or phenomena present in the actual run-time situation.
To deal with this problem, it is common in current industry practice to tune and tweak the heat transfer coefficient in a heat exchanger model (which is usually a first principle-based model) using off-line calculations that are performed on historical data collected for the plant. However, calculating or determining the heat transfer coefficient in this manner results in a delay in updating the model, meaning that the model is still typically out of tune when used in real-time. Moreover, this delay may result in incorrect tuning, as the heat transfer coefficient may have changed between the collection of historical data and the running of the plant based on the heat transfer coefficient that was tuned or tweaked based on the historical data.